1. Field of the Invention
The present invention relates generally to selecting control cohorts and more particularly, to a computer implemented method, apparatus, and computer usable program code for automatically selecting a control cohort or for analyzing individual and group healthcare data in order to provide real time healthcare recommendations.
2. Description of the Related Art
A cohort is a group of individuals, machines, components, or modules identified by a set of one or more common characteristics. This group is studied over a period of time as part of a scientific study. A cohort may be studied for medical treatment, engineering, manufacturing, or for any other scientific purpose. A treatment cohort is a cohort selected for a particular action or treatment.
A control cohort is a group selected from a population that is used as the control. The control cohort is observed under ordinary conditions while another group is subjected to the treatment or other factor being studied. The data from the control group is the baseline against which all other experimental results must be measured. For example, a control cohort in a study of medicines for colon cancer may include individuals selected for specified characteristics, such as gender, age, physical condition, or disease state that do not receive the treatment.
The control cohort is used for statistical and analytical purposes. Particularly, the control cohorts are compared with action or treatment cohorts to note differences, developments, reactions, and other specified conditions. Control cohorts are heavily scrutinized by researchers, reviewers, and others that may want to validate or invalidate the viability of a test, treatment, or other research. If a control cohort is not selected according to scientifically accepted principles, an entire research project or study may be considered of no validity wasting large amounts of time and money. In the case of medical research, selection of a less than optimal control cohort may prevent proving the efficacy of a drug or treatment or incorrectly rejecting the efficacy of a drug or treatment. In the first case, billions of dollars of potential revenue may be lost. In the second case, a drug or treatment may be necessarily withdrawn from marketing when it is discovered that the drug or treatment is ineffective or harmful leading to losses in drug development, marketing, and even possible law suits.
Control cohorts are typically manually selected by researchers. Manually selecting a control cohort may be difficult for various reasons. For example, a user selecting the control cohort may introduce bias. Justifying the reasons, attributes, judgment calls, and weighting schemes for selecting the control cohort may be very difficult. Unfortunately, in many cases, the results of difficult and prolonged scientific research and studies may be considered unreliable or unacceptable requiring that the results be ignored or repeated. As a result, manual selection of control cohorts is extremely difficult, expensive, and unreliable.
An additional problem facing those in the art of data management is computationally explosive tasks. A computer process, a comparison of data, or some other computer-implemented analysis is considered computationally explosive when the number of possible permutations in the analysis is sufficiently large that the analysis becomes impossible or undesirably slow. A simple example of a computationally explosive task is the computation of the factorial of a large number. A factorial, represented by an exclamation mark “!,” is a mathematical operation of multiplying a number by each of the integer numbers that comes before it. For example, the value of “4!” would be 4*3*2*1=24. Factorials are particularly useful in probability theory. For example, the number of possible combinations of arranging the numbers “4, 3, 2, and 1” is 4!, meaning that 24 possible order arrangements exist for those exact four numbers. The probability of randomly selecting any one of the combinations is 1/24, which corresponds to about 0.417%.
However, the factorial representation of large numbers can become computationally explosive. For example, the value of “8,000,000,000!” (the factorial of eight billion) is equal to (8,000,000,000)*(7,999,999,999)*(7,999,999,998)* . . . * 1. The multiplication of the first two numbers alone results in about the number 6.4*10e19, or 6,400,000,000,000,000,000 (or 6.4 quadrillion). Continuing the multiplication all the way to the number “1” causes the final value of “8,000,000,000!” to become truly vast.
Many other different examples of computationally explosive operations exist. For example, comparing the entire genetic sequence of a single human to the genetic sequences of a million other humans would be considered computationally explosive. The problem of the computationally explosive comparison increases exponentially if the genetic sequences of a million humans are compared to the genetic sequences of a second, different million humans. The problem increases exponentially yet again when one desires to compare these factors to other factors, such as diet, environment, and ethnicity, to attempt to determine why certain humans live longer than others.
Thus, numerically solving certain types of computationally explosive operations can be very useful. To date, no satisfactory method exists of numerically solving certain types of computationally explosive operations.